Overview

Dataset statistics

Number of variables79
Number of observations9006
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.4 MiB
Average record size in memory163.0 B

Variable types

BOOL67
NUM8
CAT4

Warnings

Dataset has 2 (< 0.1%) duplicate rows Duplicates
zip_code has a high cardinality: 182 distinct values High cardinality
on_street_name has a high cardinality: 2525 distinct values High cardinality
off_street_name has a high cardinality: 1694 distinct values High cardinality
day_of_week is highly correlated with dayHigh correlation
day is highly correlated with day_of_weekHigh correlation
hour has 396 (4.4%) zeros Zeros
minute has 1605 (17.8%) zeros Zeros

Reproduction

Analysis started2020-12-11 13:48:42.856272
Analysis finished2020-12-11 13:49:29.703152
Duration46.85 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

zip_code
Categorical

HIGH CARDINALITY

Distinct182
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
-1
2974 
11207
 
152
11236
 
121
11208
 
115
11212
 
108
Other values (177)
5536 
ValueCountFrequency (%) 
-1297433.0%
 
112071521.7%
 
112361211.3%
 
112081151.3%
 
112121081.2%
 
10467951.1%
 
11226921.0%
 
11203891.0%
 
11385881.0%
 
10457840.9%
 
Other values (172)508856.5%
 
2020-12-11T14:49:29.986164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)0.1%
2020-12-11T14:49:30.213268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.009327115
Min length2

latitude
Real number (ℝ≥0)

Distinct7248
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.72793471
Minimum40.507267
Maximum40.9109
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2020-12-11T14:49:30.676662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum40.507267
5-th percentile40.599623
Q140.6659715
median40.7168915
Q340.8020705
95-th percentile40.8657445
Maximum40.9109
Range0.403633
Interquartile range (IQR)0.136099

Descriptive statistics

Standard deviation0.08376902426
Coefficient of variation (CV)0.00205679529
Kurtosis-0.8907520713
Mean40.72793471
Median Absolute Deviation (MAD)0.0599115
Skewness0.1615208588
Sum366795.78
Variance0.007017249426
MonotocityNot monotonic
2020-12-11T14:49:30.947909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40.861862130.1%
 
40.8451990.1%
 
40.8451890.1%
 
40.7663580.1%
 
40.67573580.1%
 
40.82030580.1%
 
40.6561670.1%
 
40.6649660.1%
 
40.73353660.1%
 
40.65197460.1%
 
Other values (7238)892699.1%
 
ValueCountFrequency (%) 
40.5072671< 0.1%
 
40.5117341< 0.1%
 
40.5166241< 0.1%
 
40.5191271< 0.1%
 
40.5197221< 0.1%
 
ValueCountFrequency (%) 
40.91091< 0.1%
 
40.910761< 0.1%
 
40.910381< 0.1%
 
40.910321< 0.1%
 
40.9096071< 0.1%
 

longitude
Real number (ℝ)

Distinct6864
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.91336268
Minimum-74.23878
Maximum-73.70174
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2020-12-11T14:49:31.222902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-74.23878
5-th percentile-74.02221875
Q1-73.9586965
median-73.91749
Q3-73.8680625
95-th percentile-73.7626825
Maximum-73.70174
Range0.53704
Interquartile range (IQR)0.090634

Descriptive statistics

Standard deviation0.08299945637
Coefficient of variation (CV)-0.001122928972
Kurtosis1.211668832
Mean-73.91336268
Median Absolute Deviation (MAD)0.045295
Skewness-0.3017397742
Sum-665663.7443
Variance0.006888909757
MonotocityNot monotonic
2020-12-11T14:49:31.492455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-73.91282140.2%
 
-73.9112110.1%
 
-73.9141790.1%
 
-73.8968690.1%
 
-73.8908380.1%
 
-73.7673670.1%
 
-73.89773670.1%
 
-73.91910670.1%
 
-73.9419470.1%
 
-73.92267660.1%
 
Other values (6854)892199.1%
 
ValueCountFrequency (%) 
-74.238781< 0.1%
 
-74.235911< 0.1%
 
-74.2351151< 0.1%
 
-74.234861< 0.1%
 
-74.2304461< 0.1%
 
ValueCountFrequency (%) 
-73.701741< 0.1%
 
-73.702121< 0.1%
 
-73.702591< 0.1%
 
-73.703621< 0.1%
 
-73.706311< 0.1%
 

on_street_name
Categorical

HIGH CARDINALITY

Distinct2525
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
Belt Parkway
 
168
Broadway
 
99
Brooklyn Queens Expressway
 
90
Long Island Expressway
 
83
Atlantic Avenue
 
82
Other values (2520)
8484 
ValueCountFrequency (%) 
Belt Parkway1681.9%
 
Broadway991.1%
 
Brooklyn Queens Expressway901.0%
 
Long Island Expressway830.9%
 
Atlantic Avenue820.9%
 
Cross Bronx Expy820.9%
 
Major Deegan Expressway790.9%
 
Fdr Drive770.9%
 
Grand Central Pkwy770.9%
 
3 Avenue700.8%
 
Other values (2515)809989.9%
 
2020-12-11T14:49:31.781987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1329 ?
Unique (%)14.8%
2020-12-11T14:49:32.025431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length14
Mean length14.46824339
Min length6

off_street_name
Categorical

HIGH CARDINALITY

Distinct1694
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
Unknown
4869 
3 Avenue
 
39
Broadway
 
38
2 Avenue
 
34
4 Avenue
 
24
Other values (1689)
4002 
ValueCountFrequency (%) 
Unknown486954.1%
 
3 Avenue390.4%
 
Broadway380.4%
 
2 Avenue340.4%
 
4 Avenue240.3%
 
5 Avenue240.3%
 
Queens Boulevard200.2%
 
Atlantic Avenue200.2%
 
Park Avenue190.2%
 
Linden Boulevard170.2%
 
Other values (1684)390243.3%
 
2020-12-11T14:49:32.282363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique884 ?
Unique (%)9.8%
2020-12-11T14:49:32.512141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length7
Mean length9.774483678
Min length6

year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
2020
9005 
2017
 
1
ValueCountFrequency (%) 
20209005> 99.9%
 
20171< 0.1%
 
2020-12-11T14:49:32.697515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-11T14:49:32.826723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:33.013399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

month
Real number (ℝ≥0)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.63535421
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2020-12-11T14:49:33.198995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median11
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6422565041
Coefficient of variation (CV)0.06038882123
Kurtosis8.87277373
Mean10.63535421
Median Absolute Deviation (MAD)0
Skewness-0.9969686728
Sum95782
Variance0.412493417
MonotocityNot monotonic
2020-12-11T14:49:33.349240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
11490554.5%
 
10353439.2%
 
124925.5%
 
7260.3%
 
8230.3%
 
9220.2%
 
63< 0.1%
 
11< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
63< 0.1%
 
7260.3%
 
8230.3%
 
9220.2%
 
ValueCountFrequency (%) 
124925.5%
 
11490554.5%
 
10353439.2%
 
9220.2%
 
8230.3%
 

day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.60570731
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2020-12-11T14:49:33.531539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median17
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.70995312
Coefficient of variation (CV)0.5245156355
Kurtosis-1.070602243
Mean16.60570731
Median Absolute Deviation (MAD)7
Skewness-0.1693293974
Sum149551
Variance75.86328336
MonotocityNot monotonic
2020-12-11T14:49:33.734829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
204084.5%
 
133924.4%
 
213764.2%
 
303684.1%
 
123513.9%
 
243483.9%
 
143473.9%
 
233363.7%
 
183273.6%
 
43243.6%
 
Other values (21)542960.3%
 
ValueCountFrequency (%) 
13053.4%
 
22793.1%
 
32813.1%
 
43243.6%
 
52192.4%
 
ValueCountFrequency (%) 
311681.9%
 
303684.1%
 
293183.5%
 
282993.3%
 
272983.3%
 

day_of_week
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.60570731
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2020-12-11T14:49:33.951032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median17
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.70995312
Coefficient of variation (CV)0.5245156355
Kurtosis-1.070602243
Mean16.60570731
Median Absolute Deviation (MAD)7
Skewness-0.1693293974
Sum149551
Variance75.86328336
MonotocityNot monotonic
2020-12-11T14:49:34.170125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
204084.5%
 
133924.4%
 
213764.2%
 
303684.1%
 
123513.9%
 
243483.9%
 
143473.9%
 
233363.7%
 
183273.6%
 
43243.6%
 
Other values (21)542960.3%
 
ValueCountFrequency (%) 
13053.4%
 
22793.1%
 
32813.1%
 
43243.6%
 
52192.4%
 
ValueCountFrequency (%) 
311681.9%
 
303684.1%
 
293183.5%
 
282993.3%
 
272983.3%
 

week
Real number (ℝ≥0)

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.04585832
Minimum3
Maximum49
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2020-12-11T14:49:34.364631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile42
Q143
median45
Q347
95-th percentile49
Maximum49
Range46
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.518781434
Coefficient of variation (CV)0.0559159383
Kurtosis14.53478873
Mean45.04585832
Median Absolute Deviation (MAD)2
Skewness-1.634154283
Sum405683
Variance6.344259913
MonotocityNot monotonic
2020-12-11T14:49:34.634933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
45130814.5%
 
44121613.5%
 
42120813.4%
 
43118913.2%
 
46118713.2%
 
47111212.3%
 
4898811.0%
 
496387.1%
 
41810.9%
 
3680.1%
 
Other values (15)710.8%
 
ValueCountFrequency (%) 
31< 0.1%
 
263< 0.1%
 
274< 0.1%
 
2880.1%
 
2950.1%
 
ValueCountFrequency (%) 
496387.1%
 
4898811.0%
 
47111212.3%
 
46118713.2%
 
45130814.5%
 

hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.09882301
Minimum0
Maximum23
Zeros396
Zeros (%)4.4%
Memory size70.4 KiB
2020-12-11T14:49:34.813715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.111803487
Coefficient of variation (CV)0.4665918063
Kurtosis-0.5519085993
Mean13.09882301
Median Absolute Deviation (MAD)4
Skewness-0.4683298978
Sum117968
Variance37.35414187
MonotocityNot monotonic
2020-12-11T14:49:34.998617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
176146.8%
 
145856.5%
 
185666.3%
 
155546.2%
 
165456.1%
 
135085.6%
 
194975.5%
 
124915.5%
 
114495.0%
 
204154.6%
 
Other values (14)378242.0%
 
ValueCountFrequency (%) 
03964.4%
 
12022.2%
 
21541.7%
 
31321.5%
 
41351.5%
 
ValueCountFrequency (%) 
232903.2%
 
223353.7%
 
213263.6%
 
204154.6%
 
194975.5%
 

minute
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.74727959
Minimum0
Maximum59
Zeros1605
Zeros (%)17.8%
Memory size70.4 KiB
2020-12-11T14:49:35.212202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q340
95-th percentile54
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.98067569
Coefficient of variation (CV)0.7265718086
Kurtosis-1.204831036
Mean24.74727959
Median Absolute Deviation (MAD)15
Skewness0.0908492937
Sum222874
Variance323.3046983
MonotocityNot monotonic
2020-12-11T14:49:35.437026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0160517.8%
 
30114012.7%
 
204715.2%
 
504635.1%
 
454625.1%
 
154595.1%
 
404174.6%
 
103834.3%
 
252552.8%
 
352412.7%
 
Other values (50)311034.5%
 
ValueCountFrequency (%) 
0160517.8%
 
1460.5%
 
2470.5%
 
3440.5%
 
4640.7%
 
ValueCountFrequency (%) 
59480.5%
 
58670.7%
 
57540.6%
 
56570.6%
 
552232.5%
 

Bronx
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
7761 
1
1245 
ValueCountFrequency (%) 
0776186.2%
 
1124513.8%
 
2020-12-11T14:49:35.611796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Brooklyn
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
6927 
1
2079 
ValueCountFrequency (%) 
0692776.9%
 
1207923.1%
 
2020-12-11T14:49:35.691766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Manhattan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8134 
1
872 
ValueCountFrequency (%) 
0813490.3%
 
18729.7%
 
2020-12-11T14:49:35.754906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Queens
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
7383 
1
1623 
ValueCountFrequency (%) 
0738382.0%
 
1162318.0%
 
2020-12-11T14:49:35.821134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8793 
1
 
213
ValueCountFrequency (%) 
0879397.6%
 
12132.4%
 
2020-12-11T14:49:35.887241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8999 
1
 
7
ValueCountFrequency (%) 
0899999.9%
 
170.1%
 
2020-12-11T14:49:35.947524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8924 
1
 
82
ValueCountFrequency (%) 
0892499.1%
 
1820.9%
 
2020-12-11T14:49:36.015648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8858 
1
 
148
ValueCountFrequency (%) 
0885898.4%
 
11481.6%
 
2020-12-11T14:49:36.080359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8995 
1
 
11
ValueCountFrequency (%) 
0899599.9%
 
1110.1%
 
2020-12-11T14:49:36.494041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8721 
1
 
285
ValueCountFrequency (%) 
0872196.8%
 
12853.2%
 
2020-12-11T14:49:36.554667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8965 
1
 
41
ValueCountFrequency (%) 
0896599.5%
 
1410.5%
 
2020-12-11T14:49:36.622325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9001 
1
 
5
ValueCountFrequency (%) 
0900199.9%
 
150.1%
 
2020-12-11T14:49:36.690751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
6629 
1
2377 
ValueCountFrequency (%) 
0662973.6%
 
1237726.4%
 
2020-12-11T14:49:36.754659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8811 
1
 
195
ValueCountFrequency (%) 
0881197.8%
 
11952.2%
 
2020-12-11T14:49:36.817474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8996 
1
 
10
ValueCountFrequency (%) 
0899699.9%
 
1100.1%
 
2020-12-11T14:49:36.881055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8996 
1
 
10
ValueCountFrequency (%) 
0899699.9%
 
1100.1%
 
2020-12-11T14:49:36.944126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8996 
1
 
10
ValueCountFrequency (%) 
0899699.9%
 
1100.1%
 
2020-12-11T14:49:37.008455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8367 
1
 
639
ValueCountFrequency (%) 
0836792.9%
 
16397.1%
 
2020-12-11T14:49:37.070476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8996 
1
 
10
ValueCountFrequency (%) 
0899699.9%
 
1100.1%
 
2020-12-11T14:49:37.136059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8980 
1
 
26
ValueCountFrequency (%) 
0898099.7%
 
1260.3%
 
2020-12-11T14:49:37.200905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8458 
1
 
548
ValueCountFrequency (%) 
0845893.9%
 
15486.1%
 
2020-12-11T14:49:37.267849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Glare
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8993 
1
 
13
ValueCountFrequency (%) 
0899399.9%
 
1130.1%
 
2020-12-11T14:49:37.332031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9004 
1
 
2
ValueCountFrequency (%) 
09004> 99.9%
 
12< 0.1%
 
2020-12-11T14:49:37.399183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Illnes
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8995 
1
 
11
ValueCountFrequency (%) 
0899599.9%
 
1110.1%
 
2020-12-11T14:49:37.461027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9002 
1
 
4
ValueCountFrequency (%) 
09002> 99.9%
 
14< 0.1%
 
2020-12-11T14:49:37.523624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8991 
1
 
15
ValueCountFrequency (%) 
0899199.8%
 
1150.2%
 
2020-12-11T14:49:37.583269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8978 
1
 
28
ValueCountFrequency (%) 
0897899.7%
 
1280.3%
 
2020-12-11T14:49:37.658175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:37.724430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:37.791260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8704 
1
 
302
ValueCountFrequency (%) 
0870496.6%
 
13023.4%
 
2020-12-11T14:49:37.856007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8985 
1
 
21
ValueCountFrequency (%) 
0898599.8%
 
1210.2%
 
2020-12-11T14:49:37.926122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8958 
1
 
48
ValueCountFrequency (%) 
0895899.5%
 
1480.5%
 
2020-12-11T14:49:37.988541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8985 
1
 
21
ValueCountFrequency (%) 
0898599.8%
 
1210.2%
 
2020-12-11T14:49:38.056817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8667 
1
 
339
ValueCountFrequency (%) 
0866796.2%
 
13393.8%
 
2020-12-11T14:49:38.129706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8652 
1
 
354
ValueCountFrequency (%) 
0865296.1%
 
13543.9%
 
2020-12-11T14:49:38.197923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8992 
1
 
14
ValueCountFrequency (%) 
0899299.8%
 
1140.2%
 
2020-12-11T14:49:38.265004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8913 
1
 
93
ValueCountFrequency (%) 
0891399.0%
 
1931.0%
 
2020-12-11T14:49:38.329250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8921 
1
 
85
ValueCountFrequency (%) 
0892199.1%
 
1850.9%
 
2020-12-11T14:49:38.391406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9000 
1
 
6
ValueCountFrequency (%) 
0900099.9%
 
160.1%
 
2020-12-11T14:49:38.465889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:38.548909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8862 
1
 
144
ValueCountFrequency (%) 
0886298.4%
 
11441.6%
 
2020-12-11T14:49:38.644924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9004 
1
 
2
ValueCountFrequency (%) 
09004> 99.9%
 
12< 0.1%
 
2020-12-11T14:49:38.750676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8981 
1
 
25
ValueCountFrequency (%) 
0898199.7%
 
1250.3%
 
2020-12-11T14:49:38.845954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9004 
1
 
2
ValueCountFrequency (%) 
09004> 99.9%
 
12< 0.1%
 
2020-12-11T14:49:38.908264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8985 
1
 
21
ValueCountFrequency (%) 
0898599.8%
 
1210.2%
 
2020-12-11T14:49:38.990384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:39.050603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:39.112430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8749 
1
 
257
ValueCountFrequency (%) 
0874997.1%
 
12572.9%
 
2020-12-11T14:49:39.177569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8816 
1
 
190
ValueCountFrequency (%) 
0881697.9%
 
11902.1%
 
2020-12-11T14:49:39.242386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8829 
1
 
177
ValueCountFrequency (%) 
0882998.0%
 
11772.0%
 
2020-12-11T14:49:39.309483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8664 
1
 
342
ValueCountFrequency (%) 
0866496.2%
 
13423.8%
 
2020-12-11T14:49:39.371698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:39.440062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
9005 
1
 
1
ValueCountFrequency (%) 
09005> 99.9%
 
11< 0.1%
 
2020-12-11T14:49:39.502472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8917 
1
 
89
ValueCountFrequency (%) 
0891799.0%
 
1891.0%
 
2020-12-11T14:49:39.567897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Bike
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8540 
1
 
466
ValueCountFrequency (%) 
0854094.8%
 
14665.2%
 
2020-12-11T14:49:39.640925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Box Truck
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8681 
1
 
325
ValueCountFrequency (%) 
0868196.4%
 
13253.6%
 
2020-12-11T14:49:39.708672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Bus
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8783 
1
 
223
ValueCountFrequency (%) 
0878397.5%
 
12232.5%
 
2020-12-11T14:49:39.771160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

E-Bike
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8883 
1
 
123
ValueCountFrequency (%) 
0888398.6%
 
11231.4%
 
2020-12-11T14:49:39.839076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

E-Scooter
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8876 
1
 
130
ValueCountFrequency (%) 
0887698.6%
 
11301.4%
 
2020-12-11T14:49:39.909954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Motorcycle
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8879 
1
 
127
ValueCountFrequency (%) 
0887998.6%
 
11271.4%
 
2020-12-11T14:49:39.971579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Other
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8427 
1
 
579
ValueCountFrequency (%) 
0842793.6%
 
15796.4%
 
2020-12-11T14:49:40.036314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8666 
1
 
340
ValueCountFrequency (%) 
0866696.2%
 
13403.8%
 
2020-12-11T14:49:40.097742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sedan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
1
5697 
0
3309 
ValueCountFrequency (%) 
1569763.3%
 
0330936.7%
 
2020-12-11T14:49:40.160583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
4797 
1
4209 
ValueCountFrequency (%) 
0479753.3%
 
1420946.7%
 
2020-12-11T14:49:40.222130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Taxi
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8642 
1
 
364
ValueCountFrequency (%) 
0864296.0%
 
13644.0%
 
2020-12-11T14:49:40.290369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8855 
1
 
151
ValueCountFrequency (%) 
0885598.3%
 
11511.7%
 
2020-12-11T14:49:40.351714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Van
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
0
8887 
1
 
119
ValueCountFrequency (%) 
0888798.7%
 
11191.3%
 
2020-12-11T14:49:40.407753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-12-11T14:49:05.266745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:05.540377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:05.764529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:05.977712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:06.203188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:06.463552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:06.678653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:06.894358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:07.111932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:07.321157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:07.733026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:07.928455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:08.131514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:08.337814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:08.529181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:08.719398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:08.922339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:09.128325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:09.319351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:09.508394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:09.711799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:09.906952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:10.096532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:10.279932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:10.469247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:10.682707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:10.876504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:11.064487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:11.260700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:11.475015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:11.660909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:11.847676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:12.048491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:12.266282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:12.469905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:12.702341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:12.895099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:13.099037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:13.284803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:13.471597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:13.672864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:13.869162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:14.201667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:14.379988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:14.565834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:14.746553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:14.922038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:15.129218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:15.341742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:15.549126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:15.736047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:15.908141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:16.088670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:16.271187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:16.466460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:16.644080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:16.824788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:17.037300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:17.231149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:17.422150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:17.622739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:17.830475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:18.028093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:18.229897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-11T14:49:40.750796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-11T14:49:44.341328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-11T14:49:47.474096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-11T14:49:50.538159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-11T14:49:19.097406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-11T14:49:27.121681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

zip_codelatitudelongitudeon_street_nameoff_street_nameyearmonthdayday_of_weekweekhourminuteBronxBrooklynManhattanQueensStaten IslandAccelerator DefectiveAggressive Driving/Road RageAlcohol InvolvementAnimals ActionBacking UnsafelyBrakes DefectiveCell Phone (Hand-Held)Driver Inattention/DistractionDriver InexperienceDriverless/Runaway VehicleDrugs (Illegal)Failure To Keep RightFailure To Yield Right-Of-WayFatigued/DrowsyFell AsleepFollowing Too CloselyGlareHeadlights DefectiveIllnesLane Marking Improper/InadequateLost ConsciousnessObstruction/DebrisOther Electronic DeviceOther Lighting DefectsOther VehicularOutside Car DistractionOversized VehiclePassenger DistractionPassing Or Lane Usage ImproperPassing Too CloselyPavement DefectivePavement SlipperyPedestrian/Bicyclist/Other Pedestrian Error/ConfusionPhysical DisabilityPrescription MedicationReaction To Uninvolved VehicleShoulders Defective/ImproperSteering FailureTinted WindowsTire Failure/InadequateTow Hitch DefectiveTraffic Control Device Improper/Non-WorkingTraffic Control DisregardedTurning ImproperlyUnsafe Lane ChangingUnsafe SpeedUsing On Board Navigation DeviceVehicle VandalismView Obstructed/LimitedBikeBox TruckBusE-BikeE-ScooterMotorcycleOtherPick-Up TruckSedanStation Wagon/Sport Utility VehicleTaxiTractor Truck DieselVan
0-140.798504-73.967125West 103 StreetUnknown202012334913370000000000000000000000000000000000000000000000000000000000000000000
1-140.731167-73.709940256 Street87 Avenue20201222491900000000000000000000000000000000000000000000000000000000000000000100
21137540.735550-73.85097062-60 108 StreetUnknown2020113030499400001000000000000000000000000000000000000000000000000000000000000000
3-140.701527-73.989570Brooklyn Queens ExpresswayUnknown2020112929485450000000000000000000100000000000000000000000000000000000000000010000
4-140.700108-73.953830Wallabout StreetUnknown20201126264823300000000000001000000000000000000000000000000000000000000000000010000
51121540.668293-73.9792406 StreetUnknown20201123234811280100000000001000000000000000000000000000000000000000000000000010000
6-140.624640-74.141670Forest AvenueDecker Avenue20201122224720100000000000000000000000000000000000000000000000000000000000000000000
7-140.677483-73.930330Utica AvenueUnknown2020112020471200000000000001000000000000000010000000000000000000000001000000000100
81001040.736706-73.978220East 23 StreetUnknown2020111818471100010000000000000000000000000000000000000000000001000000000000000000
9-140.608757-74.038086Verrazano Bridge LowerUnknown2017117173320000000000000000000000000000000000000000000000000000000000000010000

Last rows

zip_codelatitudelongitudeon_street_nameoff_street_nameyearmonthdayday_of_weekweekhourminuteBronxBrooklynManhattanQueensStaten IslandAccelerator DefectiveAggressive Driving/Road RageAlcohol InvolvementAnimals ActionBacking UnsafelyBrakes DefectiveCell Phone (Hand-Held)Driver Inattention/DistractionDriver InexperienceDriverless/Runaway VehicleDrugs (Illegal)Failure To Keep RightFailure To Yield Right-Of-WayFatigued/DrowsyFell AsleepFollowing Too CloselyGlareHeadlights DefectiveIllnesLane Marking Improper/InadequateLost ConsciousnessObstruction/DebrisOther Electronic DeviceOther Lighting DefectsOther VehicularOutside Car DistractionOversized VehiclePassenger DistractionPassing Or Lane Usage ImproperPassing Too CloselyPavement DefectivePavement SlipperyPedestrian/Bicyclist/Other Pedestrian Error/ConfusionPhysical DisabilityPrescription MedicationReaction To Uninvolved VehicleShoulders Defective/ImproperSteering FailureTinted WindowsTire Failure/InadequateTow Hitch DefectiveTraffic Control Device Improper/Non-WorkingTraffic Control DisregardedTurning ImproperlyUnsafe Lane ChangingUnsafe SpeedUsing On Board Navigation DeviceVehicle VandalismView Obstructed/LimitedBikeBox TruckBusE-BikeE-ScooterMotorcycleOtherPick-Up TruckSedanStation Wagon/Sport Utility VehicleTaxiTractor Truck DieselVan
89961143440.656160-73.76736Rockaway BoulevardBrewer Boulevard202011224512200001000000000000000000000000000000000000000000000000000000000001000
89971046840.860850-73.90545Aqueduct AvenueUnknown20201144451801000000000000000000000000000000000000000000000000000000000000001000
89981143340.704388-73.77917180 Street105 Avenue20201112124618200001000000001000000000000000000000000000000000000000000000000011000
89991003140.829020-73.94485Amsterdam AvenueWest 151 Street2020111818477300010000000001000000000000000000000000000000000000000000000000010000
90001121740.686500-73.98787Hoyt StreetDean Street20201113134621430100000000000000000000000000000000000000000000000000000000000010000
9001-140.658535-73.97328West DriveCenter Drive202011444512100000000000000000000000000000000000000000000000000000001000000000000
90021169140.598297-73.7482814-05 New Haven AvenueUnknown2020111818479150001000000001000000000000000000000000000000000000000000000000001000
90031121840.640415-73.9859339 StreetUnknown202011444511240100000000000000000000000000000000000000000000000000000100000001000
90041142740.735300-73.7368182-25 234 StreetUnknown2020111313461250001000000000000000000000000000000000000000010000000000000000011000
90051136840.750225-73.85515111 Street42 Avenue2020113345800001000000000000000000000000000000000000000000000000000000000010000

Duplicate rows

Most frequent

zip_codelatitudelongitudeon_street_nameoff_street_nameyearmonthdayday_of_weekweekhourminuteBronxBrooklynManhattanQueensStaten IslandAccelerator DefectiveAggressive Driving/Road RageAlcohol InvolvementAnimals ActionBacking UnsafelyBrakes DefectiveCell Phone (Hand-Held)Driver Inattention/DistractionDriver InexperienceDriverless/Runaway VehicleDrugs (Illegal)Failure To Keep RightFailure To Yield Right-Of-WayFatigued/DrowsyFell AsleepFollowing Too CloselyGlareHeadlights DefectiveIllnesLane Marking Improper/InadequateLost ConsciousnessObstruction/DebrisOther Electronic DeviceOther Lighting DefectsOther VehicularOutside Car DistractionOversized VehiclePassenger DistractionPassing Or Lane Usage ImproperPassing Too CloselyPavement DefectivePavement SlipperyPedestrian/Bicyclist/Other Pedestrian Error/ConfusionPhysical DisabilityPrescription MedicationReaction To Uninvolved VehicleShoulders Defective/ImproperSteering FailureTinted WindowsTire Failure/InadequateTow Hitch DefectiveTraffic Control Device Improper/Non-WorkingTraffic Control DisregardedTurning ImproperlyUnsafe Lane ChangingUnsafe SpeedUsing On Board Navigation DeviceVehicle VandalismView Obstructed/LimitedBikeBox TruckBusE-BikeE-ScooterMotorcycleOtherPick-Up TruckSedanStation Wagon/Sport Utility VehicleTaxiTractor Truck DieselVancount
01120140.694794-73.982796Johnson StreetUnknown2020112121479001000000000000000000000000000000001000000000000000000000000000010002
11120540.698020-73.974990Flushing AvenueUnknown2020115545163001000000000010000000000000000000000000000000000000000000000010100002